class_docstr: Named entity recognition with huggingface backbones. Inherit from HFAutoModelForTextPrediction.
class_docstr_tok: ["Named","entity","recognition","with","huggingface","backbones",".","Inherit","from","HFAutoModelForTextPrediction","."]
code: def get_output_dict(
      self,
      sequence_output: torch.Tensor,
      logits: torch.Tensor,
      logits_label: torch.Tensor,
      token_word_mapping: torch.Tensor,
      word_offsets: torch.Tensor,
      column_features: Optional[Dict[str, torch.Tensor]] = None,
      column_feature_masks: Optional[Dict[str, torch.Tensor]] = None,
  ):
      ret = {COLUMN_FEATURES: {FEATURES: {}, MASKS: {}}}
      if column_features != None:
          ret[COLUMN_FEATURES][FEATURES].update(column_features)
          ret[COLUMN_FEATURES][MASKS].update(column_feature_masks)
  
      ret.update(
          {
              LOGITS: logits,
              FEATURES: sequence_output,  # input of ner fusion model
              NER_ANNOTATION: logits_label,
              TOKEN_WORD_MAPPING: token_word_mapping,
              WORD_OFFSETS: word_offsets,
          }
      )
  
      return {self.prefix: ret}
  
file_path: autogluon/multimodal/src/autogluon/multimodal/models/ner_text.py
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from transformers import logging
  from ..constants import AUTOMM, COLUMN, COLUMN_FEATURES, FEATURES, LABEL, LOGITS, MASKS, NER_ANNOTATION, TEXT_SEGMENT_IDS, TEXT_TOKEN_IDS, TEXT_VALID_LENGTH, TOKEN_WORD_MAPPING, WORD_OFFSETS
  from .huggingface_text import HFAutoModelForTextPrediction
  from .utils import assign_layer_ids, get_column_features, get_pretrained_tokenizer
label_desc: Use image node_id 6 for calling the HFAutoModelForNER obj's underlying member method code with example usage: obj.get_output_dict(sequence_output, logits, logits_label, token_word_mapping, word_offsets, column_features, column_feature_masks) and returns: dict